Knowledge Graph Inference for spoken dialog systems

Abstract

We propose Inference Knowledge Graph, a novel approach of remapping existing, large scale, semantic knowledge graphs into Markov Random Fields in order to create user goal tracking models that could form part of a spoken dialog system. Since semantic knowledge graphs include both entities and their attributes, the proposed method merges the semantic dialog-state-tracking of attributes and the database lookup of entities that fulfill users' requests into one single unified step. Using a large semantic graph that contains all businesses in Bellevue, WA, extracted from Microsoft Satori, we demonstrate that the proposed approach can return significantly more relevant entities to the user than a baseline system using database lookup.